Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

Subray, Siddhartha; Tschimben, Stefan; Gifford, Kevin

doi:10.1109/access.2021.3087113

Cited by 11 publications

(3 citation statements)

References 34 publications

(33 reference statements)

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“…Autoencoders were mainly developed and used for feature extraction to reduce the high dimensionality of datasets to be ready for classification by different ML algorithms [ 54 – 57 ]. They have also been utilized in a variety of applications, including anomaly detection in different types of applications [ 58 – 61 ] and classification problems in many applications [ 62 – 64 ]. Since they are considered non-linear feature reduction methods, autoencoders have superior performance when compared to other linear feature reduction approaches such as PCA [ 65 , 66 ].…”

Section: Methodsmentioning

confidence: 99%

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

et al. 2023

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Background In the current genomic era, gene expression datasets have become one of the main tools utilized in cancer classification. Both curse of dimensionality and class imbalance problems are inherent characteristics of these datasets. These characteristics have a negative impact on the performance of most classifiers when used to classify cancer using genomic datasets. Results This paper introduces Reduced Noise-Autoencoder (RN-Autoencoder) for pre-processing imbalanced genomic datasets for precise cancer classification. Firstly, RN-Autoencoder solves the curse of dimensionality problem by utilizing the autoencoder for feature reduction and hence generating new extracted data with lower dimensionality. In the next stage, RN-Autoencoder introduces the extracted data to the well-known Reduced Noise-Synthesis Minority Over Sampling Technique (RN- SMOTE) that efficiently solve the problem of class imbalance in the extracted data. RN-Autoencoder has been evaluated using different classifiers and various imbalanced datasets with different imbalance ratios. The results proved that the performance of the classifiers has been improved with RN-Autoencoder and outperformed the performance with original data and extracted data with percentages based on the classifier, dataset and evaluation metric. Also, the performance of RN-Autoencoder has been compared to the performance of the current state of the art and resulted in an increase up to 18.017, 19.183, 18.58 and 8.87% in terms of test accuracy using colon, leukemia, Diffuse Large B-Cell Lymphoma (DLBCL) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. Conclusion RN-Autoencoder is a model for cancer classification using imbalanced gene expression datasets. It utilizes the autoencoder to reduce the high dimensionality of the gene expression datasets and then handles the class imbalance using RN-SMOTE. RN-Autoencoder has been evaluated using many different classifiers and many different imbalanced datasets. The performance of many classifiers has improved and some have succeeded in classifying cancer with 100% performance in terms of all used metrics. In addition, RN-Autoencoder outperformed many recent works using the same datasets.

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Section: Methodsmentioning

confidence: 99%

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

et al. 2023

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“…One limitation of the JED method is that it currently only works for classification and detection problems. For a time-series prediction problem like the one defined in [38] or an autoencoder used for classification like those seen in [5,39] alternative decision criteria would need to be developed. Figure 10.…”

Section: "Just Enough" Decision Makingmentioning

confidence: 99%

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Moore

Buehrer

Headley

2022

Sensors

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Recurrent neural networks have been shown to outperform other architectures when processing temporally correlated data, such as from wireless communication signals. However, compared to other architectures, such as convolutional neural networks, recurrent neural networks can suffer from drastically longer training and evaluation times due to their inherent sample-by-sample data processing, while traditional usage of both of these architectures assumes a fixed observation interval during both training and testing, the sample-by-sample processing capabilities of recurrent neural networks opens the door for alternative approaches. Rather than assuming that the testing and observation intervals are equivalent, the observation intervals can be “decoupled” or set independently. This can potentially reduce training times and will allow for trained networks to be adapted to different applications without retraining. This work illustrates the benefits and considerations needed when “decoupling” these observation intervals for spectrum sensing applications, using modulation classification as the example use case. The sample-by-sample processing of RNNs also allows for the relaxation of the typical requirement of a fixed time duration of the signals of interest. Allowing for variable observation intervals is important in real-time applications like cognitive radio where decisions need to be made as quickly and accurately as possible as well as in applications like electronic warfare in which the sequence length of the signal of interest may be unknown. This work examines a real-time post-processing method called “just enough” decision making that allows for variable observation intervals. In particular, this work shows that, intuitively, this method can be leveraged to process less data (i.e., shorter observation intervals) for simpler inputs (less complicated signal types or channel conditions). Less intuitively, this works shows that the “decoupling” is dependent on appropriate training to avoid bias and ensure generalization.

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“…Automatic modulation classification for radio signals is attractive because it is widely used in numerous applications such as radio monitoring, 1 spectrum sharing, 2 and communication reconnaissance 3 . It is also an important step before signal demodulation, decoding, and interference in military communications and other sensitive applications 4 .…”

Section: Introductionmentioning

confidence: 99%

Shift‐invariant universal adversarial attacks to avoid deep‐learning‐based modulation classification

Qian

Wang

et al. 2023

Int J Communication

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Summary With the development of deep‐learning technology, how to prevent signal modulation from being correctly classified by deep‐learning‐based intruders becomes a challenging issue. Adversarial attack provides an ideal solution as deep‐learning models are proved to be vulnerable to intentionally designed perturbations. However, applying adversarial attacks to communication systems faces several practical problems such as shift‐invariant, imperceptibility, and bandwidth compatibility. To this end, a shift‐invariant universal adversarial attack approach is proposed in this work for misleading deep‐learning‐based modulation classifiers used by intruders. Specifically, this work first introduces a convolutional neural network (CNN)‐based UAP (universal adversarial perturbation) generation model that contains an finite impulse response (FIR) filter layer to control the bandwidth of the output perturbation. Besides, this work proposes a circular shift scheme that simulates the random signal cropping in the inference phase and thus ensures the shift‐invariant property of adversarial perturbations. In addition, this work designs a composite loss function that improves the imperceptibility of the adversarial perturbation in both time and frequency domains without decreasing the effectiveness of the adversarial attack. Experimental results demonstrate the effectiveness of the proposed approach, achieving about 50% accuracy drop on the target model when the perturbation‐to‐signal ratio (PSR) is −10 dB. Furthermore, extensive experiments are conducted to validate the shift‐invariant, imperceptibility, bandwidth compatibility, and transferability of the proposed approach for modulation classification tasks.

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Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

Cited by 11 publications

References 34 publications

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data

Decoupling RNN Training and Testing Observation Intervals for Spectrum Sensing Applications

Shift‐invariant universal adversarial attacks to avoid deep‐learning‐based modulation classification

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